Elevated mammographic density (MD) is one of the strongest risk factors for sporadic breast cancer. Epidemiologic evidence suggests that MD is, in part, genetically determined; however, the relationship between MD and BRCA1/2 mutation status is equivocal. We compared MD in unaffected BRCA1/2 mutation carriers enrolled in the U.S. National Cancer Institute’s Clinical Genetics Branch’s Breast Imaging Study (n = 143) with women at low-to-average breast cancer risk enrolled in the same study (n = 29) or the NCI/National Naval Medical Center’s Susceptibility to Breast Cancer Study (n = 90). The latter were BRCA mutation-negative members of mutation-positive families or women with no prior breast cancer, a Pedigree Assessment Tool score <8 (i.e., low risk of a hereditary breast cancer syndrome) and a Gail score <1.67. A single experienced mammographer measured MD using a computer-assisted thresholding method. We collected standard breast cancer risk factor information in both studies. Unadjusted mean percent MD was higher in women with BRCA1/2 mutations compared with women at low-to-average breast cancer risk (37.3% vs. 33.4%; P = 0.04), but these differences disappeared after adjusting for age and body mass index (34.9% vs. 36.3%; P = 0.43). We explored age at menarche, nulliparity, age at first birth, menopausal status, number of breast biopsies, and exposure to exogenous hormonal agents as potential confounders of the MD and BRCA1/2 association. Taking these factors into account did not significantly alter the results of the age/body mass index-adjusted analysis. Our results do not provide support for an independent effect of BRCA1/2 mutation status on mammographic density.

IntroductionMammographic density is similar among women at risk of either sporadic or BRCA1/2-related breast cancer. It has been suggested that digitized mammographic images contain computer-extractable information within the parenchymal pattern, which may contribute to distinguishing between BRCA1/2 mutation carriers and non-carriers.MethodsWe compared mammographic texture pattern features in digitized mammograms from women with deleterious BRCA1/2 mutations (n = 137) versus non-carriers (n = 100). Subjects were stratified into training (107 carriers, 70 non-carriers) and testing (30 carriers, 30 non-carriers) datasets. Masked to mutation status, texture features were extracted from a retro-areolar region-of-interest in each subject’s digitized mammogram. Stepwise linear regression analysis of the training dataset identified variables to be included in a radiographic texture analysis (RTA) classifier model aimed at distinguishing BRCA1/2 carriers from non-carriers. The selected features were combined using a Bayesian Artificial Neural Network (BANN) algorithm, which produced a probability score rating the likelihood of each subject’s belonging to the mutation-positive group. These probability scores were evaluated in the independent testing dataset to determine whether their distribution differed between BRCA1/2 mutation carriers and non-carriers. A receiver operating characteristic analysis was performed to estimate the model’s discriminatory capacity.ResultsIn the testing dataset, a one standard deviation (SD) increase in the probability score from the BANN-trained classifier was associated with a two-fold increase in the odds of predicting BRCA1/2 mutation status: unadjusted odds ratio (OR) = 2.00, 95% confidence interval (CI): 1.59, 2.51, P = 0.02; age-adjusted OR = 1.93, 95% CI: 1.53, 2.42, P = 0.03. Additional adjustment for percent mammographic density did little to change the OR. The area under the curve for the BANN-trained classifier to distinguish between BRCA1/2 mutation carriers and non-carriers was 0.68 for features alone and 0.72 for the features plus percent mammographic density.ConclusionsOur findings suggest that, unlike percent mammographic density, computer-extracted mammographic texture pattern features are associated with carrying BRCA1/2 mutations. Although still at an early stage, our novel RTA classifier has potential for improving mammographic image interpretation by permitting real-time risk stratification among women undergoing screening mammography.Electronic supplementary materialThe online version of this article (doi:10.1186/s13058-014-0424-8) contains supplementary material, which is available to authorized users.

Purpose: Ductal lavage has been used for risk stratification and biomarker development and to identify intermediate endpoints for risk-reducing intervention trials. Little is known about patient characteristics associated with obtaining nipple aspirate fluid (NAF) and adequate cell counts (z10 cells) in ductal lavage specimens from BRCA mutation carriers. Methods: We evaluated patient characteristics associated with obtaining NAF and adequate cell counts in ductal lavage specimens from the largest cohort of women from BRCA families yet studied (BRCA1/2 = 146, mutation-negative = 23, untested = 2). Fisher's exact test was used to evaluate categorical variables; Wilcoxon nonparametric test was used to evaluate continuous variables associated with NAF or ductal lavage cell count adequacy. Logistic regression was used to identify independent correlates of NAF and ductal lavage cell count adequacy. Results: From 171 women, 45 (26%) women had NAF and 70 (41%) women had ductal lavage samples with z10 cells. Postmenopausal women with intact ovaries compared with premenopausal women [odds ratio (OR), 4.8; P = 0.03] and women without a prior breast cancer history (OR, 5.2; P = 0.04) had an increased likelihood of yielding NAF. Having breast-fed (OR, 3.4; P = 0.001), the presence of NAF before ductal lavage (OR, 3.2; P = 0.003), and being premenopausal (OR, 3.0; P = 0.003) increased the likelihood of ductal lavage cell count adequacy. In known BRCA1/2 mutation carriers, only breast-feeding (OR, 2.5; P = 0.01) and the presence of NAF (OR, 3.0; P = 0.01) were independent correlates of ductal lavage cell count adequacy. Conclusions: Ductal lavage is unlikely to be useful in breast cancer screening among BRCA1/2 mutation carriers because the procedure fails to yield adequate specimens sufficient for reliable cytologic diagnosis or to support translational research activities.

IntroductionMammographic density is similar among women at risk of either sporadic or BRCA1/2-related breast cancer. It has been suggested that digitized mammographic images contain computer-extractable information within the parenchymal pattern, which may contribute to distinguishing between BRCA1/2 mutation carriers and non-carriers.MethodsWe compared mammographic texture pattern features in digitized mammograms from women with deleterious BRCA1/2 mutations (n = 137) versus non-carriers (n = 100). Subjects were stratified into training (107 carriers, 70 non-carriers) and testing (30 carriers, 30 non-carriers) datasets. Masked to mutation status, texture features were extracted from a retro-areolar region-of-interest in each subject’s digitized mammogram. Stepwise linear regression analysis of the training dataset identified variables to be included in a radiographic texture analysis (RTA) classifier model aimed at distinguishing BRCA1/2 carriers from non-carriers. The selected features were combined using a Bayesian Artificial Neural Network (BANN) algorithm, which produced a probability score rating the likelihood of each subject’s belonging to the mutation-positive group. These probability scores were evaluated in the independent testing dataset to determine whether their distribution differed between BRCA1/2 mutation carriers and non-carriers. A receiver operating characteristic analysis was performed to estimate the model’s discriminatory capacity.ResultsIn the testing dataset, a one standard deviation (SD) increase in the probability score from the BANN-trained classifier was associated with a two-fold increase in the odds of predicting BRCA1/2 mutation status: unadjusted odds ratio (OR) = 2.00, 95% confidence interval (CI): 1.59, 2.51, P = 0.02; age-adjusted OR = 1.93, 95% CI: 1.53, 2.42, P = 0.03. Additional adjustment for percent mammographic density did little to change the OR. The area under the curve for the BANN-trained classifier to distinguish between BRCA1/2 mutation carriers and non-carriers was 0.68 for features alone and 0.72 for the features plus percent mammographic density.ConclusionsOur findings suggest that, unlike percent mammographic density, computer-extracted mammographic texture pattern features are associated with carrying BRCA1/2 mutations. Although still at an early stage, our novel RTA classifier has potential for improving mammographic image interpretation by permitting real-time risk stratification among women undergoing screening mammography.Electronic supplementary materialThe online version of this article (doi:10.1186/s13058-014-0424-8) contains supplementary material, which is available to authorized users.

Background: Ductal lavage (DL) has been proposed as a minimally-invasive, well-tolerated tool for obtaining breast epithelial cells for cytological evaluation of breast cancer risk. We report DL tolerability in BRCA1/2 mutation-positive and -negative women from an IRB-approved research study.